\newcommand{\UnivarScriptName}{\texttt{\tt Univar-Stats.dml}}
\newcommand{\BivarScriptName}{\texttt{\tt bivar-stats.dml}}

\newcommand{\OutputRowIDMinimum}{1}
\newcommand{\OutputRowIDMaximum}{2}
\newcommand{\OutputRowIDRange}{3}
\newcommand{\OutputRowIDMean}{4}
\newcommand{\OutputRowIDVariance}{5}
\newcommand{\OutputRowIDStDeviation}{6}
\newcommand{\OutputRowIDStErrorMean}{7}
\newcommand{\OutputRowIDCoeffVar}{8}
\newcommand{\OutputRowIDQuartiles}{?, 13, ?}
\newcommand{\OutputRowIDMedian}{13}
\newcommand{\OutputRowIDIQMean}{14}
\newcommand{\OutputRowIDSkewness}{9}
\newcommand{\OutputRowIDKurtosis}{10}
\newcommand{\OutputRowIDStErrorSkewness}{11}
\newcommand{\OutputRowIDStErrorCurtosis}{12}
\newcommand{\OutputRowIDNumCategories}{15}
\newcommand{\OutputRowIDMode}{16}
\newcommand{\OutputRowIDNumModes}{17}
\newcommand{\OutputRowText}[1]{\mbox{(output row~{#1})\hspace{0.5pt}:}}

\newcommand{\NameStatR}{Pearson's correlation coefficient}
\newcommand{\NameStatChi}{Pearson's~$\chi^2$}
\newcommand{\NameStatPChi}{$P\textrm{-}$value of Pearson's~$\chi^2$}
\newcommand{\NameStatV}{Cram\'er's~$V$}
\newcommand{\NameStatEta}{Eta statistic}
\newcommand{\NameStatF}{$F$~statistic}
\newcommand{\NameStatRho}{Spearman's rank correlation coefficient}

Descriptive statistics are used to quantitatively describe the main characteristics of the data.
They provide meaningful summaries computed over different observations or data records
collected in a study.  These summaries typically form the basis of the initial data exploration
as part of a more extensive statistical analysis.  Such a quantitative analysis assumes that
every variable (also known as, attribute, feature, or column) in the data has a specific
\emph{level of measurement}~\cite{Stevens1946:scales}.

The measurement level of a variable, often called as {\bf variable type}, can either be
\emph{scale} or \emph{categorical}.  A \emph{scale} variable represents the data measured on
an interval scale or ratio scale.  Examples of scale variables include `Height', `Weight',
`Salary', and `Temperature'.  Scale variables are also referred to as \emph{quantitative}
or \emph{continuous} variables.  In contrast, a \emph{categorical} variable has a fixed
limited number of distinct values or categories.  Examples of categorical variables
include `Gender', `Region', `Hair color', `Zipcode', and `Level of Satisfaction'.
Categorical variables can further be classified into two types, \emph{nominal} and
\emph{ordinal}, depending on whether the categories in the variable can be ordered via an
intrinsic ranking.  For example, there is no meaningful ranking among distinct values in
`Hair color' variable, while the categories in `Level of Satisfaction' can be ranked from
highly dissatisfied to highly satisfied.

The input dataset for descriptive statistics is provided in the form of a matrix, whose
rows are the records (data points) and whose columns are the features (i.e.~variables).
Some scripts allow this matrix to be vertically split into two or three matrices.  Descriptive
statistics are computed over the specified features (columns) in the matrix.  Which
statistics are computed depends on the types of the features.  It is important to keep
in mind the following caveats and restrictions:
\begin{Enumerate}
\item  Given a finite set of data records, i.e.~a \emph{sample}, we take their feature
values and compute their \emph{sample statistics}.  These statistics
will vary from sample to sample even if the underlying distribution of feature values
remains the same.  Sample statistics are accurate for the given sample only.
If the goal is to estimate the \emph{distribution statistics} that are parameters of
the (hypothesized) underlying distribution of the features, the corresponding sample
statistics may sometimes be used as approximations, but their accuracy will vary.
\item  In particular, the accuracy of the estimated distribution statistics will be low
if the number of values in the sample is small.  That is, for small samples, the computed
statistics may depend on the randomness of the individual sample values more than on
the underlying distribution of the features.
\item  The accuracy will also be low if the sample records cannot be assumed mutually
independent and identically distributed (i.i.d.), that is, sampled at random from the
same underlying distribution.  In practice, feature values in one record often depend
on other features and other records, including unknown ones.
\item  Most of the computed statistics will have low estimation accuracy in the presence of
extreme values (outliers) or if the underlying distribution has heavy tails, for example
obeys a power law.  However, a few of the computed statistics, such as the median and
\NameStatRho{}, are \emph{robust} to outliers.
\item  Some sample statistics are reported with their \emph{sample standard errors}
in an attempt to quantify their accuracy as distribution parameter estimators.  But these
sample standard errors, in turn, only estimate the underlying distribution's standard
errors and will have low accuracy for small or \mbox{non-i.i.d.} samples, outliers in samples,
or heavy-tailed distributions.
\item  We assume that the quantitative (scale) feature columns do not contain missing
values, infinite values, \texttt{NaN}s, or coded non-numeric values, unless otherwise
specified.  We assume that each categorical feature column contains positive integers
from 1 to the number of categories; for ordinal features, the natural order on
the integers should coincide with the order on the categories.
\end{Enumerate}

\input{DescriptiveUnivarStats}

\input{DescriptiveBivarStats}

\input{DescriptiveStratStats}
